Speaker
Description
Since the first direct detection of gravitational waves in 2015, multi-messenger astronomy has sought to probe compact object mergers through complementary messengers across the electromagnetic spectrum and with neutrinos. Neutrino detectors have searched for signals in temporal coincidence with gravitational wave events across both low- and high-energy regimes. While no significant coincident neutrino signals have been observed to date, experiments have placed upper limits on the neutrino fluence associated with binary mergers.
SNO+ is a kilo-tonne scale liquid scintillator (LS) neutrino detector located 2 km underground at SNOLAB in Sudbury, Ontario. Its large target mass and radiopure LS enables sensitivity to MeV-scale neutrinos across multiple physics programs. We present a search for low-energy neutrinos in temporal coincidence with gravitational wave events reported by the LIGO-Virgo-KAGRA (LVK) collaboration during the O4 observing run, using data from SNO+'s liquid scintillator phase.
The analysis exploits three detection channels: inverse beta decay (IBD), neutrino-proton elastic scattering (ν-p ES), and neutrino-electron elastic scattering (ν-e ES), chosen for their distinct signatures and expected sensitivity to MeV-scale emission. Symmetric ΔT coincidence windows are examined around confirmed gravitational wave events, and the expected background is estimated using off-time sidebands. Detection efficiencies are evaluated using detailed Monte Carlo simulations incorporating supernova-motivated and monoenergetic neutrino spectra, full detector response, event reconstruction, and channel-specific selection criteria.
This work describes the analysis framework, background estimation strategy, and signal modeling used to search for MeV-scale neutrino emission associated with compact binary mergers during O4. This study demonstrates the sensitivity of large LS detectors to low-energy multi-messenger signals and establishes the methodology for deriving fluence constraints under assumed spectral models.